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Abstract

Thermal ablation treatment is a relatively new method for prostate cancer therapy, where high energy focused ultrasound is used to thermally coagulate cancerous tissue. Thermal ablation is advantageous in comparison to other invasive treatment methods such as surgery, radiation therapy, or chemotherapy due to its minimally invasive nature. Currently, magnetic resonance thermal imaging (MRTI) is used to monitor the temperature distribution during the ablation treatment. However, MRTI is not only an expensive method, but it is also difficult to set up. Ultrasound strain imaging, on the other hand, has proven to be an effective method to detect and track lesion boundaries in soft tissues such as the liver and breast. Several strain-imaging algorithms have been developed, each one tailored for certain application and tissue geometry. In this thesis, we develop a new algorithm based on the cross-correlation method to potentially detect and track lesions during a prostate ablation treatment. In order to verify the performance of the algorithm and mimic the desired clinical scenario, we used three different ultrasound phantoms to verify the performance of the developed algorithm in terms of speed, resolution and accuracy. The results show that using the ultrasound data taken by a sector scanner, the algorithm can generate strain images with millimeter-order spatial resolution and enough contrast level to allow for detection of the lesion.